A method for adaptive performance improvement of operating systems

Abstract

This paper presents a method for dynamic modification of operating system control parameters to improve system performance. Improved parameter settings are learned by experimenting on the system. The experiments compare the performance of alternative parameter settings in each region of a partitioned load-performance space associated with the system. The results are used to modify important control parameters periodically, responding to fluctuations in system load and performance. The method can be used to implement adaptive tuning, to choose between alternative algorithms and policies, or to select the best fixed settings for parameters which are not modified. The method was validated and proved practical by an investigation of two parameters governing core quantum allocation on a Sperry Univac 1100 system. This experiment yielded significant results, which are presented and discussed. Directions for future research include automating the method, determining the effect of simultaneous modifications to unrelated control parameters, and detecting dominant control parameters.